The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library(lubridate)
Attaching package: 'lubridate'
The following objects are masked from 'package:base':
date, intersect, setdiff, union
library(tsibble)
Registered S3 method overwritten by 'tsibble':
method from
as_tibble.grouped_df dplyr
Attaching package: 'tsibble'
The following object is masked from 'package:lubridate':
interval
The following objects are masked from 'package:base':
intersect, setdiff, union
# Example: Cache la Poudre River at Mouth (USGS site 06752260)poudre_flow <-readNWISdv(siteNumber ="06752260", # Download data from USGS for site 06752260parameterCd ="00060", # Parameter code 00060 = discharge in cfs)startDate ="2013-01-01", # Set the start dateendDate ="2023-12-31") |># Set the end daterenameNWISColumns() |># Rename columns to standard names (e.g., "Flow", "Date")mutate(Date =yearmonth(Date)) |># Convert daily Date values into a year-month format (e.g., "2023 Jan")group_by(Date) |># Group the data by the new monthly Datesummarise(Flow =mean(Flow)) # Calculate the average daily flow for each month
The STL decomposition shows three main patterns in the streamflow data. The trend shows a slow increase in flow from 2013 to 2015, followed by a decline through 2020, and a slight rise again after that. The seasonal component shows a strong and consistent annual cycle, with peaks in late spring or early summer probably caused by snowmelt. Lastly, the remainder shows short-term fluctuations not explained by trend or seasonality—maybe from storms, droughts, or water management events.